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Learning-Augmented Mechanism Design for Facility Location under $L_p$-Norm Social Costs

NeurIPS 2026 · Learning theory and algorithms › Submodular and game algorithms · approach: Matching markets and welfare

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AI-extracted summary. Written by AI from the paper's abstract only. Numbers and claims can be wrong or missing context, so check the paper before you cite or build on it.

Designs learning-augmented strategyproof mechanisms for single-facility location on the line that use a prediction of the optimal facility location to approximately minimize L_p-norm social cost.

Problem

Strategyproof facility location on the line under L_p-norm social cost, where agents may misreport locations and the mechanism has access only to an imperfect prediction of the optimal facility location.

Approach

Augments classical strategyproof facility location mechanisms with a prediction of the optimal facility location, aiming for mechanisms whose approximation ratio is good when the prediction is accurate and degrades gracefully otherwise, for L_p social costs with p in (1, infinity).

Experimental setup

Metrics: approximation ratio

Builds on: learning-augmented algorithms strategyproof facility location on the line

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